%0 Journal Article
%T Medical Image Segmentation Based on Finite Mixture Models of Non-parametric Multivariate Chebyshev Orthogonal Polynomials<br>多元Chebyshev正交多项式混合模型及其在医学图像分割中的应用
%A 刘哲
%A 宋余庆
%A 宋收珊
%J 计算机科学
%D 2013
%I 
%X To solve the problem of over reliance on priori assumptions of the parameter methods for finite mixture mo dcls and the problem that monk Chebyshev orthogonal polynomials can only process the gray images, a segmentation method of mixture models of multivariate Chebyshev orthogonal polynomials for color image was proposed in this pa- per. First, the multivariate Chebyshev orthogonal polynomials was derived by the Fourier analysis and the tensor pro- duct theory, and the nonparametric mixture model of multivariate orthogonal polynomials was proposed. And the mean integrated squared error(MISE) was used to estimate the smoothing parameter for each model. Second, the expectation maximum(EM) algorithm was used to estimate the orthogonal polynomial coefficients and the model of the weight. hhis method does not require any prior assumptions on the model, and it can effectively overcome the "model mismatch" problem. hhe experimental results with the images show that this method can achieve better segmentation results than the mean-shift method.
%K Non-parametric mixture models
%K Image segmentation
%K Smoothing parameter
%K Multivariate orthogonal polynomial<br>非参数混合模型，图像分割，平滑参数，多元正交多项式
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=D8CF0D2A1D9D33610353DB856064DD2B&yid=FF7AA908D58E97FA&vid=1371F55DA51B6E64&iid=0B39A22176CE99FB&sid=69E4C201C13601F9&eid=E2B9962CCD971A0D&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0